Tag Archives: Data analysis

Part Deux – 4 ways your analytics can deceive you

Last week I kicked off a series of posts about how analytics can deceive you.  Today I continue this thread with some concrete, public examples of how organizations have “missed the boat”

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We have seen four trends in our prospects and clients which have led to (in some cases) disastrous results.

The first is related to data missing from an analysis. To make informed decisions, you need to synthesize all the relevant information into your decisions. Companies have made vast improvements in terms of incorporating internal data but there is at risk of getting blindsided by information that can only be found in external data – like weather, Dun and Bradstreet, economic indicators or social media.

In April of this year, a major US airline had an unfortunate incident where a passenger was physically removed from a plane. The company could see the metrics related to social media activity but by the way they were reacting, it became clear they were missing information around social sentiment. The initial incident was rough enough but made worse as the CEO made a cold, victim blaming speech. The airline lost a BILLION dollars in market cap in under 8 hours – not because of the incident itself but as a reaction to the CEO’s statement blaming the passenger.  If only they had been more attuned with the sentiment of their long time customers, they could have reacted faster and headed off the stock slide. The cost of not knowing.  (See more examples of social media fails here)

The second is related to incorrect data. Excel remains the BI tool of choice for many business users and analysts and there are no shortage of stories where a transposed number, missing decimal, or issue with a minus sign wreaked havoc.  In fact, a Forbes article suggests that “excel might be the most dangerous software on the planet.”

Today, we see manual checks built into processes that involve manual entry. However, the bigger problem lies in places where companies have outgrown legacy systems and use complex Excel models to perform calculations and transform numbers as part of a workflow. The very nature of excel is that the calculation lives in each cell and no mechanism can ensure accuracy. A complex workbook can have thousands of calculations. A MarketWatch article entitled 88% of Spreadsheet Have Errors, cautions that “Spreadsheets, even careful development, contain errors in 1% or more of all formula cells.”

This is what happened to a large US based financial services company. They had outgrown their accounting system and inserted a complex Excel model into a process that exported all open positions into Excel so they could be priced at current market rates and the values were returned back into a work stream. Unbeknownst to them at the time, there was an error in one of the calculations that led to Fannie Mae overstating revenue by more than $1B. When they announced the correction, their stock dropped $2.25 per share. The cost of not knowing.

These are two examples of common problems many companies face – MISSING DATA, or not including all relevant data points in an analysis and INCORRECT DATA, just blatantly having the wrong information at hand.  Both of these have have led to significant meltdowns for their respective organizations.  The internet is littered with stories similar to these.

Check them out Google:

  • Social Media fails
  • why excel is bad for data analysis

So what the heck do you do with Watson Analytics?

It has been a little over a year since IBM’s Watson Analytics was released.  Watson Analytics, a smart data discovery tool, continues to deliver easy analytics for everyone.  Some competitors are trying to catch and imitate what IBM has delivered but frankly, their latest news is more “snooze worthy” than newsworthy.

Because Watson Analytics is so easy to use and visually appealing, IBM has seen strong adoption (1 million plus users!).  Over the past couple of months there have been some amazing additions to an already fantastic product (see Watson Analytics for Social Media).

Watson Analytics is so powerful and can address so many different uses cases.  I am frequently faced with the following query:  So, I get it!  I have a ton of data and I need a way to make sense of it.  But what exactly do I DO with it??

This really depends on what your role is.  In our work lives we each fit into some type of role:

  • sales guy
  • marketeer
  • finance professional 
  • operations director
  • supply chain manager
  • head of strategy

So, as you read this and think about what YOU can do with Watson Analytics, think about the data you need to better visualize.  Think about what finding patterns within your data could do for you.  Finally, think about how adding social media data to your analyses can make you analyses event more effective.

But most importantly let’s put this in context of some of the major roles and use cases that will matter to you:

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Over the next several weeks, I will be sharing the details behind some of these specific use cases and providing a “how to do this yourself” approach.  Check back to hear more about how #watsonanalytics can help address many of these use cases.

 

 

Analytics is not a thing you “do”…it’s a way of life.

I recently attended conference in Singapore

A beautiful view of Singapore at night.
A beautiful view of Singapore at night.

where I had the chance to listen to thought leaders discuss the need for analytics to address what Gartner refers to as the “nexus of forces” – CloudSocialMobile and Information.   Mychelle Mollot took a deeper dive on the “information”  force in the Big Data and Analytics keynote.

As a marketer I grapple with explaining these nexus of forces and the need for Big Data and Analytics everyday.  Everyone seems to have an opinion on exactly what Big Data is and why it is so important.   It was in fact several customers and partners  who reminded me of several things: Continue reading Analytics is not a thing you “do”…it’s a way of life.